A post-personnel matching system based on deep fusion

Abstract

The application relates to the technical field of artificial intelligence, in particular to a post-talent matching system based on deep fusion, which comprises a graph neural network talent evaluation module, a heterogeneous data fusion module, a deep neural network index construction module, a matching priority adjustment module and an attribution trace analysis module.In the application, post demand and talent characteristic data are deeply fused, more accurate and personalized matching is realized, potential relationships in complex characteristic data can be identified, higher-quality post matching is provided, and the matching is no longer limited to traditional simple docking, but deeply mines the deep-level matching degree of posts and talents, solves the problems of single matching and poor flexibility of traditional methods, effectively adjusts the matching priority by combining the matching evolution cycle and fluctuation analysis, ensures that the matching process is more in line with the actual needs and behavior characteristics of posts and talents, improves the matching accuracy, reduces the enterprise recruitment decision risk, and finally improves the matching degree of posts and talents.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a job-talent matching system based on deep fusion. Background Technology

[0002] Artificial intelligence (AI) technology refers to the use of computers to simulate human intelligent activities, aiming to solve a range of problems related to human cognition through the design of algorithms, models, and systems. This field encompasses several core technologies, including machine learning, deep learning, natural language processing, image recognition, and intelligent recommendation. Machine learning is one of the core areas, involving learning and reasoning from data. Commonly used techniques include supervised learning, unsupervised learning, and reinforcement learning. Deep learning, as an important method in machine learning, processes information by simulating the brain's neural networks and is widely used in industries such as speech recognition, image classification, and autonomous driving. The field of AI also includes multimodal data fusion, intelligent decision-making systems, robotics, intelligent search, and recommendation systems, striving to improve the intelligence level of systems through various means.

[0003] Traditional job-talent matching systems rely on basic matching rules based on job requirements and talent backgrounds to establish a match between companies and individuals. Traditional methods typically employ simple rule-based matching, manually matching candidates based on job requirements and basic information such as education, work experience, and skills listed in their resumes. This approach suffers from limitations in matching accuracy and flexibility, and fails to comprehensively consider the deep compatibility between job requirements and individual talent needs and abilities. To address these technical challenges, deeply integrated job-talent matching systems have emerged. These systems utilize deep learning and other artificial intelligence technologies to analyze and process more complex and diverse feature data from job requirements and talent information, such as career development potential, work habits, and interests, further improving the match between jobs and talent. This system primarily uses a multi-layered neural network model to fuse multi-dimensional data from job requirements and talent, achieving more accurate and personalized matching.

[0004] Existing technologies often neglect personalized needs and deeper capabilities in the job-talent matching process. Traditional matching methods rely on simple rule-based matching, primarily processing basic information such as educational background, work experience, and skills. They fail to comprehensively identify the deep matching relationship between jobs and candidates across multiple dimensions, including behavior and interests. Because this approach is overly dependent on manual rules and static data, it fails to consider dynamic factors, resulting in simplistic and inflexible matching results, and an inability to adjust matching priorities in real time. For example, when an applicant's behavior changes, the system may fail to detect these fluctuations in a timely manner, causing companies to miss out on top talent or prematurely abandon high-potential candidates, thus impacting recruitment effectiveness and the long-term development of talent. Summary of the Invention

[0005] To address the aforementioned technical issues, this application proposes a job-talent matching system based on deep integration.

[0006] The technical solution adopted in this application is: a job-talent matching system based on deep integration, comprising:

[0007] The graph neural network talent evaluation module is used to obtain key information on job requirements and talent characteristics, analyze the relationship between job and talent, determine the matching direction and ability chain length based on the response time difference and priority order of job requirements and talent capabilities, and perform feature weight and time delay mapping to generate a talent capability structure map.

[0008] Heterogeneous data fusion module: Based on the talent capability structure map, select the capability feature values ​​and behavior frequency of core nodes, match and compare the abnormal fluctuation frequency and feature weight, identify the key matching level, and obtain the node matching priority scale;

[0009] Deep Neural Network Index Construction Module: Based on the node matching priority scale, extract the time interval and duration of job requirements and talent feedback, determine the high-frequency matching distribution stage, classify the periodic trends of similar matching events, and generate a matching evolution cycle sequence.

[0010] Matching Priority Adjustment Module: Calls the matching evolution cycle sequence, combines the abnormal fluctuation value and response time delay within the jurisdiction, filters the target positions and talents in the high trend segment, revises the matching cycle value in segments, and obtains the position-talent matching cycle adjustment table.

[0011] Furthermore, the graph neural network talent evaluation module includes:

[0012] The competency feature extraction submodule is used to obtain key information on job requirements and talent characteristics, align multiple signals by timestamp, extract talent capability mutation points, locate changes in requirements and characteristics, identify response differences, and generate job-talent response deviation.

[0013] Capability Lag Analysis Submodule: Based on the job-talent response deviation, extract the capability signal delay segment and the characteristic signal response sequence, count the delay length and response sequence, determine the signal matching direction, measure the number of capability chain trigger sequences within the lag segment, and generate the capability signal lag structure quantity.

[0014] Capability chain structure generation submodule: Based on the hysteresis structure of capability signals, extract behavioral feature change points and capability chain signal linkage time periods, compare signal response frequency and time delay, identify linkage times and average delay ratio, analyze matching relationships and capability response frequency mapping, and generate talent capability structure map.

[0015] Furthermore, the heterogeneous data fusion module includes:

[0016] Feature fluctuation extraction submodule: Based on the talent capability structure map, extract the capability feature values ​​of core nodes, screen sequences whose fluctuations exceed the feature fluctuation benchmark value, analyze the proportion of abnormal fluctuations, and generate the abnormal feature fluctuation frequency ratio.

[0017] Frequency weight matching submodule: By calling the frequency ratio of abnormal feature fluctuations, combined with the frequency of node operations and the number of capability paths, the frequency weight comparison item is identified, the node abnormal offset is adjusted according to the proportion of tasks on the capability path, and the fluctuation coupling priority index is calculated.

[0018] Priority Level Determination Submodule: Based on the fluctuation coupling priority index, extract the numerical range corresponding to the node, set the node level division interval group, assign level identifiers according to the index value falling into the interval, sort to determine the matching level, and obtain the node matching priority scale.

[0019] Furthermore, the deep neural network metric construction module includes:

[0020] Matching record screening submodule: Based on the node matching priority scale, collect the trigger and end time points of job requirements and talent feedback, identify the time interval between adjacent matches, filter records that exceed the matching interval benchmark value, and generate a matching time interval sequence;

[0021] Matching cycle segmentation submodule: By calling the matching time interval sequence, the number of consecutive matches of the same type of matching event is counted. Based on the matching duration, interval time and demand load index, the matching cycle distribution trend value is calculated, and the matching cycle distribution stage is established.

[0022] The cycle trend classification submodule identifies matching events of the same cycle trend by calling the matching cycle distribution stage, identifies differences in trend fluctuations, classifies and archives them according to the difference threshold, and generates a matching evolution cycle sequence.

[0023] Furthermore, the matching priority adjustment module includes:

[0024] Trend sequence identification submodule: By calling the matching evolution cycle sequence, it extracts incremental change values, judges the upward trend range based on the cumulative increase and fluctuation amplitude, marks key positions and talents, and generates an automated matching trend anomaly identification list;

[0025] Response Feature Extraction Submodule: By calling the job number and talent number in the automated matching trend anomaly identification list, it identifies abnormal fluctuation values ​​and response delays within the segment. Combining the delay distribution and fluctuation frequency, it analyzes the differences in job and talent responses to obtain the automated matching response feature difference value.

[0026] The periodic revision submodule identifies the periodic revision item structure based on the difference values ​​of the automated matching response characteristics and the corresponding period of the trend segment, analyzes the degree of periodic offset and frequency distribution amplitude, calculates the periodic revision offset, and adjusts the frequency and time distribution of job-talent matching based on the differences in the original periodic structure to obtain the job-talent matching periodic adjustment table.

[0027] Furthermore, it also includes:

[0028] Attribution and source analysis module: Based on the job-talent matching cycle adjustment table, it collects the response difference and behavioral fluctuation characteristics of frequently matched jobs and talents, determines whether the fluctuation characteristics have the same origin, identifies matching deviation nodes, and forms a matching deviation source tracing path map.

[0029] Furthermore, the attribution analysis module includes:

[0030] The response difference extraction submodule: Based on the job-talent matching cycle adjustment table, it examines the response time and behavior change sequence of high-frequency jobs and talents, identifies the synchronization deviation between response time difference and behavior change rate, filters the time points where the deviation exceeds the stable range and the job and talent numbers, and generates an automated matching response offset list.

[0031] The wave origin discrimination submodule: Based on the automated matching response offset list, it calls the behavior wave sequence, compares the wave amplitude and direction at the offset time point, identifies continuous and consistent wave segments and records the intervals that overlap with the offset time point, and generates a response wave origin segment table.

[0032] Deviation attribution identification submodule: Based on the response fluctuation common source segment table, extract the node path of job position and talent number, track the response sequence and signal transmission of nodes in the overlapping segment, identify the abnormal response frequency of the signal source node, and form a matching deviation tracing path map.

[0033] Furthermore, the formula for calculating the volatility coupling priority index is as follows:

[0034] ;

[0035] in, Represents a volatility coupling priority indicator. Representing the Frequency of operations per node Representing the Number of capability paths per node Representing the The frequency weight ratio of each node, The weighted value representing the frequency of node operations and the number of capability paths. Representative task percentage coefficient, This represents the total number of nodes.

[0036] Furthermore, the formula for calculating the matching periodic distribution trend value is as follows:

[0037] ;

[0038] in, The value representing the periodic distribution trend of the matching. This represents the total number of matching events of the same type. Representing the Duration of the matching event This represents the average duration of all matched events;

[0039] Furthermore, the formula for calculating the periodic revision offset is as follows:

[0040] ;

[0041] in, Indicates the periodic revision offset. Indicates the first The difference in response characteristics over each period, Indicates the first The expected response characteristic difference value for each cycle. For the number of cycles, This represents the average of the differences in response characteristics across all periods. Indicates the first The frequency distribution amplitude of each cycle This is for adjusting the coefficient.

[0042] The advantages of this application over existing technologies are as follows: By deeply integrating various job requirements and talent characteristic data, it can achieve more accurate and personalized matching. When processing more complex characteristic data, it can identify the potential relationship between job requirements and talent background, providing higher-quality job matching. By analyzing multi-dimensional characteristics such as talent interests, habits, and career development potential, the matching process is no longer limited to traditional simple data exchange, but delves into the deep-seated fit between jobs and talent. This solves the problems of traditional methods being singular and inflexible in matching. By combining matching evolution cycle and fluctuation analysis, it can effectively adjust matching priorities and identify potential deviations, ensuring that the matching process better matches the actual needs and behavioral characteristics of jobs and talent. This improves matching accuracy, reduces the risk of corporate recruitment decisions, and results in a higher degree of job-talent matching. Attached Figure Description

[0043] The following description, in conjunction with the accompanying drawings, further illustrates this application:

[0044] Figure 1 This is a system flowchart for this application;

[0045] Figure 2 This is a system framework diagram of this application;

[0046] Figure 3 This is a flowchart of the neural network talent evaluation module in this application;

[0047] Figure 4 This is a flowchart of the heterogeneous data fusion module in this application;

[0048] Figure 5 Here is a flowchart of the deep neural network metric construction module in this application;

[0049] Figure 6 Here is a flowchart of the matching priority adjustment module in this application;

[0050] Figure 7 This is a flowchart of the attribution analysis module in this application. Detailed Implementation

[0051] like Figures 1 to 7 As shown, this application provides a job-talent matching system based on deep integration, including:

[0052] The graph neural network talent evaluation module is used to obtain key information on job requirements and talent characteristics, analyze the relationship between job and talent, determine the matching direction and ability chain length based on the response time difference and priority order of job requirements and talent capabilities, and perform feature weight and time delay mapping to generate a talent capability structure map.

[0053] The neural network talent evaluation module, as a core component of the job-talent matching system, is responsible for abstracting the complex relationship between job requirements and talent capabilities into a structured graph form, enabling accurate and efficient talent evaluation and matching. In this module, the nodes in the graph structure represent specific talent capabilities or behavioral characteristics, such as capability nodes like "Java programming," "microservice architecture design," and "database optimization," and behavioral nodes like "course learning records," "code submission frequency," and "behavioral change trends." Each node carries multi-dimensional feature information such as capability level, behavioral frequency, capability mutation time, and abnormal fluctuation frequency ratio. The edges in the graph express the dependencies and transmission paths between nodes, specifically reflecting the growth order and task linkage structure of talent capabilities. The direction of the edge indicates the order of capability improvement, while the edge weight integrates response time latency (e.g., the number of days from job posting to actual capability improvement), linkage frequency (e.g., the number of times collaboration occurs between tasks and capabilities), and average response latency ratio (e.g., the ratio of the time to complete a related capability task to the standard task cycle). The module uses a "competency feature extraction submodule" to align job requirements with talent characteristics over time, extracting abrupt change points in competency and calculating response deviation values. Then, the "competency lag analysis submodule" identifies the lag structure of competency responses, such as delay length and competency chain triggering order. Finally, the "competency chain structure generation submodule" extracts behavioral change points and linkage periods, combining frequency and latency to construct a complete "talent competency structure map." This map not only depicts the current competency level of the talent but also reveals the degree of matching between competency growth paths, behavioral habits, and job requirements. It provides standardized and quantifiable input for subsequent modules such as heterogeneous data fusion, matching cycle prediction, and priority adjustment, greatly enhancing the talent-job matching system's ability to identify complex competency structures and its intelligent decision-making capabilities.

[0054] Heterogeneous data fusion module: Based on the talent capability structure map, select the capability feature values ​​and behavior frequency of core nodes, match and compare the abnormal fluctuation frequency and feature weight, identify the key matching level, and obtain the node matching priority scale;

[0055] Deep Neural Network Index Construction Module: Based on the node matching priority scale, extract the time interval and duration of job requirements and talent feedback, determine the high-frequency matching distribution stage, classify the periodic trends of similar matching events, and generate a matching evolution cycle sequence.

[0056] Matching Priority Adjustment Module: Calls the matching evolution cycle sequence, combines the abnormal fluctuation value and response time delay in the jurisdiction, filters the target positions and talents in the high trend segment, and revises the matching cycle value in segments to obtain the position-talent matching cycle adjustment table.

[0057] Attribution and source analysis module: Based on the job-talent matching cycle adjustment table, it collects the response difference and behavioral fluctuation characteristics of frequently matched jobs and talents, determines whether the fluctuation characteristics have the same origin, identifies matching deviation nodes, and forms a matching deviation source tracing path map.

[0058] The talent capability structure map includes matching direction classification, capability chain path structure, and feature weight distribution; the node matching priority scale includes matching level labels, node fit coefficient, and priority score range; the matching evolution cycle sequence includes matching interval distribution, matching duration frequency, and cycle classification results; the job-talent matching cycle adjustment table includes target job list, cycle revision parameters, and delay correlation factor; and the matching deviation source tracing path map includes fluctuation homogeneous feature groups, deviation node location results, and source tracing path structure map.

[0059] The "capability chain path structure" in the talent capability structure map corresponds to the capability response sequence and linkage path extracted in the "capability lag analysis submodule"; the "matching direction classification" is the positive or negative matching result obtained by comparing the job requirement signal timestamp with the talent capability improvement time; the "feature weight distribution" is reflected in the mapping of behavioral response frequency to node weight. The "matching level label" in the node matching priority table comes from the calculation of the fluctuation coupling priority index and the classification of levels according to the set interval; the "node fit coefficient" may reflect the fit between the index value and the matching requirements; and the "priority scoring interval" comes from the quartile analysis setting of the distribution of 500 sample data. The "matching interval distribution" and "matching duration frequency" of the matching evolution cycle sequence are collected and analyzed in time series form in the "matching record screening submodule"; and the "cycle classification result" is the product of classifying and archiving multiple trend values ​​according to fluctuation differences. The "cycle revision parameter" and "delay correlation factor" in the job-talent matching cycle adjustment table are specifically derived from the calculation process of response characteristic difference value and cycle revision offset; and the target job list is the set of key jobs marked in the trend identification stage. Finally, the “fluctuation homogeneous feature group” and “deviation node location result” mentioned in the “matching deviation source tracing path diagram” correspond to the overlapping segments and their associated nodes that change in the same direction in the behavior sequence, respectively, while the “source tracing path structure diagram” is finally constructed by tracking the node response sequence and identifying the abnormal frequency of the signal source.

[0060] Please see Figure 2 , 3 The graph neural network talent evaluation module includes:

[0061] The competency feature extraction submodule is used to obtain key information on job requirements and talent characteristics, align multiple signals by timestamp, extract talent capability mutation points, locate changes in requirements and characteristics, identify response differences, and generate job-talent response deviation.

[0062] A "talent capability mutation point" refers to a moment when an individual's performance in a particular skill undergoes a significant change within a short period, typically reflecting a qualitative leap in their skill level over a given timeframe. This mutation is not a minor natural fluctuation, but rather a critical point where the skill value exhibits a jump or decline in a continuous time series, representing a structurally significant change in a skill, knowledge, or behavioral performance. From a systems perspective, identifying capability mutation points involves performing time-series analysis on talent capability characteristic signals, aligning them with job requirement signals, and detecting whether a significant change has occurred in the characteristic value of a talent in a certain capability dimension around a specific time stamp—for example, a rise in level from "entry-level" to "proficient level," or from "proficient level" to "expert level." Such changes often indicate that the talent has achieved a leap in skill through training, project experience, performance evaluation, or job promotion. Therefore, the system defines such transitional nodes as "capability mutation points," serving as a crucial basis for matching judgments and capability mapping construction. In general, the ability mutation point emphasizes the "turning point" in the development of talent capabilities. Its essence is to structurally capture significant changes in the trajectory of capability growth, and to provide a more dynamic and realistic reference for capability expression for job matching.

[0063] For example, given the key information that the job requirement for "Senior Java Engineer" specifies a "microservice architecture design" skill level of 4 (expert level) and that talent "Zhang San's" actual performance level in this skill in project A is 2 (proficient level), we first align the two signal streams—the job requirement signal {timestamp: 01-15, skill: microservice architecture design, requirement level: 4} and the talent characteristic signal {timestamp: 01-15, skill: microservice architecture design, performance level: 1; timestamp: 03-20, skill: microservice architecture design, performance level: 2}—according to the period from 01-01 to 06-30. We find that at timestamp 03-20, talent "Zhang San's" skill characteristic signal value changes from 1 (entry-level) to 20-20. The jump to 2 is defined here as the talent capability mutation point. This mutation point originates from the completion of the "Distributed Systems" course in March 2025. Subsequently, it was located that the "Microservice Architecture Design" capability requirement level in the job posting released on January 15 was 4, while the talent's capability at that time was 1. Its characteristic change lagged behind the requirement. Then, by calculating the difference between the requirement level 4 and the talent capability level 2 at the time point of March 20, that is, 4-2=2, this difference was identified as the response difference. Finally, the response differences of each capability item were collected into a vector [2, 1, 3], where 2 is the deviation of "Microservice Architecture Design", 1 is the deviation of "Database Optimization", and 3 is the deviation of "Containerized Deployment", generating the job-talent response deviation amount.

[0064] The "time stamp period from 01-01 to 06-30" refers to an observation time window used to analyze changes in job requirements and talent capabilities. The timestamp format is "month-day", that is, "01-01" represents January 1st and "06-30" represents June 30th. This means that the system performs time alignment and dynamic tracking of job requirement signals and talent capability characteristic signals within this continuous six-month period, and observes their changing trends and response differences.

[0065] Taking the talent "Zhang San" as an example, the system detected that he completed the "Distributed Systems" course in March. This event was marked as a turning point in his "Microservice Architecture Design" ability—his ability level improved from "Beginner" to "Proficient." This course learning information did not come from a traditional static resume, but rather relied on the system's dynamic integration of multi-source heterogeneous data, including online learning platform records, training system logs, employee self-reporting platforms, and automated data interfaces from internal skills certification systems. Traditional resumes are often outdated and fail to reflect real-time changes in talent capabilities. This system, by integrating internal and external data sources, achieves real-time perception and accurate extraction of dynamic behaviors such as talent learning and skill advancement, thereby establishing a more timely and in-depth linkage between job requirements and talent development.

[0066] Capability Lag Analysis Submodule: Based on the job-talent response deviation, extract the capability signal delay segment and the characteristic signal response sequence, count the delay length and response sequence, determine the signal matching direction, measure the number of capability chain trigger sequences within the lag segment, and generate the capability signal lag structure quantity.

[0067] "Capability chain triggering order" refers to the sequence in which various related capabilities are activated or improved during the talent development process, reflecting the dependencies and progressive paths between capabilities. In job requirements, different capabilities often do not exist in isolation but constitute an interconnected capability chain. For example, mastering "database optimization" may be a prerequisite for achieving "microservice architecture design." The system identifies the improvement time points of each capability by comparing the time series between job requirements and talent capability responses, and determines the chronological order in which these capabilities are actually improved. This sequence is the "capability chain triggering order." This order not only reflects the talent's response rhythm to the job's capability structure but also reveals their learning path and capability transfer logic, thus providing a basis for judging the fit of the job's capability chain and inferring their potential growth path. For example, if the system detects that a talent improves "database optimization" before "microservice architecture design," and this is consistent with the dependency order required by the job, then the capability chain triggering order can be considered reasonable, helping to improve matching accuracy. Conversely, if the triggering order contradicts the logic of the job competency chain, it may mean that the competency preparation is insufficient or the growth path is deviated, which will be reflected in the graph neural network as poor path mapping or low node weight.

[0068] Based on the aforementioned job-talent response deviation [2, 1, 3], the signal delay segments and characteristic signal response sequences for each capability are first extracted. For example, for the "microservice architecture design" capability, the job requirement was raised on January 15th, while the talent capability only significantly improved on March 20th, forming a delay segment from January 15th to March 20th. The length of this delay segment is calculated to be 64 days, and the talent capability improvement sequence is recorded as first improving "database optimization" capability (February 10th), then improving "microservice architecture design" capability (March 20th). Next, the signal matching direction is determined. If the demand signal timestamp is earlier than the talent capability improvement timestamp, it is considered a positive match, such as "microservice architecture design" in this example. Otherwise, it is a negative match. Subsequently, within this 64-day lag period, the triggering order of the capability chain is determined. Specifically, the improvement of "database optimization" capability (preceding capability) precedes the improvement of "microservice architecture design" capability (target capability). The number of triggering orders is recorded as 2. The structured data such as the delay length of 64 days, the response order ["database optimization", "microservice architecture design"] and the number of triggering orders 2 are integrated to generate the capability signal lag structure quantity.

[0069] Capability chain structure generation submodule: Based on the hysteresis structure of capability signals, extract behavioral feature change points and capability chain signal linkage time periods, compare signal response frequency and time delay, identify linkage times and average delay ratio, analyze matching relationships and capability response frequency mapping, and generate talent capability structure map.

[0070] "Behavioral characteristic turning points" refer to key moments within a certain time window where an individual's behavioral patterns or data undergo significant changes, revealing inflection points in their ability status or development trend. These turning points typically manifest as moments when an individual's behavioral data deviates significantly from the norm in frequency, magnitude, or direction during learning, work, or task participation. Specifically, the following situations can be identified as behavioral characteristic turning points: First, the completion of a key course, such as the end of a "distributed systems" course, signifies a potential leap in their related abilities; second, a significant increase or decrease in task completion frequency within a short period, such as a sudden increase in code submissions from 2 to 5 times within a week, or a sudden decrease to 0 times; third, fluctuations in behavioral records related to specific skills (such as reading technical documents, answering online questions, or code quality scores) within a certain time period, such as a score jumping from 80 to 95 points; fourth, a change in task type or content, such as a sudden shift from "feature development" to "architectural design," signifying a leap in the level of ability application. The system accurately locates these behavioral characteristic changes by cross-referencing time series analysis and multi-source behavioral data, and then correlates them with job competency requirements. This is further used to construct a talent competency structure map and competency chain structure, reflecting the talent growth path and its dynamic fit with the job.

[0071] Based on the 64-day delay length and response sequence recorded in the capability signal lag structure, we first extracted the behavioral characteristic variable of talent "Zhang San," namely, completing the "Distributed Systems" course in March, and linked it to the linkage period of the "Microservice Architecture Design" capability chain signal, namely from March 20th to April 20th. Then, we compared the response frequency of tasks related to this capability (such as API gateway configuration) during this period, which was 3 times per week, while the delay for talent to complete related learning tasks was an average of 5 days. Subsequently, we identified that the effective linkage between the behavioral characteristic variable and the capability signal was 4 times during this linkage period. By calculating the ratio of average delay to task cycle, i.e., 5 days / 7 days ≈ 0.71, we obtained the average delay ratio. We analyzed the matching relationship between the talent's capability improvement sequence and the capability dependency path of the job requirements (the job requires mastering containerized deployment first, and then mastering microservice architecture). We found that the order of the two is inconsistent, forming a mismatch relationship. We then mapped the talent's capability response frequency (3 times per week) to the node weight of the capability graph. Finally, we integrated all the above structured information to generate a talent capability structure graph.

[0072] Please see Figure 2 , 4 The heterogeneous data fusion module includes:

[0073] Feature fluctuation extraction submodule: Based on the talent capability structure map, extract the capability feature values ​​of core nodes, screen sequences whose fluctuations exceed the feature fluctuation benchmark value, analyze the proportion of abnormal fluctuations, and generate the abnormal feature fluctuation frequency ratio.

[0074] Based on the talent competency structure map, core nodes associated with the position "Senior Java Engineer" were extracted, namely the time series of competency feature values ​​for "Java Programming," "Spring Framework," and "MySQL Database." For example, the competency feature value sequence for the "Java Programming" node over the past six months was [4.5, 4.6, 4.7, 4.2, 4.3, 4.4]. The feature fluctuation baseline was set at 0.3. This baseline was determined by statistically analyzing the standard deviation of the competency value sequences of 100 engineers at the same level over the past year, taking the mean of 0.28 and then increasing it by 5%. This setting process ensured that the baseline could reflect the general fluctuation level of the group. The specific experimental process was as follows: monthly competency assessment data of 100 engineers were collected for 12 consecutive months. In each month, the standard deviation of each engineer's ability sequence is calculated, and then the average of these 100 standard deviations is calculated, resulting in 0.28. The final benchmark value is set as 0.28×(1+5%)≈0.3. Then, sequences with fluctuations exceeding the characteristic fluctuation benchmark value are selected. The fluctuation value of the "Java Programming" node from the 3rd to the 4th month, |4.2-4.7|=0.5, is compared with the benchmark value of 0.3. Since 0.5>0.3, it is determined to be an abnormal fluctuation. Further analysis shows that the "Java Programming" node has one abnormal fluctuation in the six-month observation period, with a total of 5 observation points (five fluctuations between six data points). The percentage of abnormal fluctuations is calculated to be 1 / 5=0.2. Finally, this indicator of each core node is integrated to generate the abnormal characteristic fluctuation frequency ratio.

[0075] Frequency weight matching submodule: By calling the frequency ratio of abnormal feature fluctuations, combined with the frequency of node operations and the number of capability paths, the frequency weight comparison item is identified, the node abnormal offset is adjusted according to the proportion of tasks on the capability path, and the fluctuation coupling priority index is calculated using formula (1).

[0076] (1);

[0077] in, Represents a volatility coupling priority indicator. Representing the Node operation frequency (unit: times / week). Representing the Number of capability paths per node (unit: paths) Representing the Frequency weight ratio of each node (unitless). The weighted value representing the frequency of node operations and the number of capability paths. Representative task percentage coefficient, This represents the total number of nodes.

[0078] "Node task frequency" refers to the execution frequency of actual work tasks associated with a specific capability node within a unit of time, usually quantified in terms of "times / week" or "times / month". Each node represents a specific capability, such as "Java programming" or "database optimization", and the task frequency reflects the activity level of that capability during the execution of real projects. For example, if a "Java programming" node involves 4 development tasks per week in a project, its node task frequency is 4 times / week. This indicator not only reflects the application density of the capability in actual work but also serves as a reference for assessing the importance and urgency of the capability. In the system, node task frequency, along with parameters such as the number of capability paths and the ratio of abnormal fluctuation frequency, constitutes the calculation factor of the "fluctuation coupling priority indicator". This indicator can identify core capability nodes that need to be prioritized and matched. Therefore, node task frequency is one of the important dynamic features in the talent capability map that measures the strong correlation between the intensity of skill practice and the actual needs of the job, directly affecting subsequent matching priority assessment and cycle prediction analysis.

[0079] The frequency ratio of abnormal feature fluctuations was used, with "Java Programming" at 0.2, "Spring Framework" at 0.1, and "MySQL Database" at 0.4. This was combined with the node job frequency collected from project management and the number of capability paths extracted from the capability map. Specific data is shown in Table 1. Subsequently, frequency weight comparison items were identified, namely the job frequency, number of capability paths, and frequency weight ratio for each node. The calculation method is to divide the frequency ratio of abnormal feature fluctuations of a node by the sum of the frequency ratios of abnormal feature fluctuations of all nodes. For example, the frequency weight ratio of node 1 (Java programming) is... The frequency weight ratio of node 2 to node 3 is calculated in the same way. , Next, the abnormal offset of the node is adjusted according to the proportion of tasks on the capability path. Here, formula (1) is used for calculation. The operation logic of this formula is that the numerator is to quantify the comprehensive activity of each node in the dynamic environment by multiplying and summing the three factors: the frequency of operation, the number of capability paths, and the frequency weight ratio. The square root term of the denominator is used as a normalization factor to balance the dimensional influence of the two different parameters, the number of capability paths and the frequency weight. The final product term is... This adjusts the node activity from a global perspective, using parameters... The calculation method is as follows The weight and The values ​​were set to 0.6 and 0.4 respectively. These values ​​were derived by performing regression analysis on data from 50 historical projects to determine the contribution of frequency and path number to project success rate. As a task weighting coefficient, it was set with reference to the company's internal quantitative scoring of the importance of various tasks in the "Senior Java Engineer" job description, with a total score of 100 points, of which core development tasks account for 75%. The coefficient was set to 0.75, and this setting was jointly reviewed and confirmed by the department head and project manager to ensure its rationality.

[0080] Table 1 Node Parameter Table

[0081]

[0082] Table 1 lists the parameters of each node used for calculation. Substituting the data, the calculation is performed as follows: First, the frequency weight ratio of each node is calculated. :

[0083] ;

[0084] ;

[0085] ;

[0086] Calculate the weighted value : ;

[0087] Calculation formula numerator : ;

[0088] Calculation formula denominator part : ;

[0089] Finally, calculate the volatility coupling priority index. :

[0090] ;

[0091] The advantage of the formula is that by coupling and weighting the abnormal fluctuation frequency that reflects the dynamic changes in talent ability with the frequency of tasks and the number of paths that reflect the coreness of their work tasks and the breadth of ability associations, and by introducing task proportions for global adjustment, the indicator can more accurately identify the ability nodes that need the most attention and matching in actual work, and finally calculate the fluctuation coupling priority indicator.

[0092] Priority Level Determination Submodule: Based on the fluctuation coupling priority index, extract the numerical range corresponding to the node, set the node level division interval group, assign level identifiers according to the index value falling into the interval, sort to determine the matching level, and obtain the node matching priority scale.

[0093] Based on the calculation result of the fluctuation coupling priority index 282.32, first extract the numerical interval corresponding to this index value, and preset a node level division interval group. The setting of this interval group is determined based on the quartile analysis of the fluctuation coupling priority index distribution of 500 past talent samples. Specifically, it is divided into: high priority (C > 250), medium priority (150 < C ≤ 250), low priority (C ≤ 150). Compare the calculation result 282.32 with this interval group. Since 282.32 > 250, the core ability node sets such as "Java programming" are assigned the level identifier of "high priority". Then sort all the core ability nodes in descending order according to their index values, determine their matching levels, and finally form a list containing each node and its priority to obtain the node matching priority scale.

[0094] Please refer to Figure 2 、 5 , the deep neural network index construction module includes:

[0095] Matching record screening sub-module: According to the node matching priority scale, collect the trigger and end time points of the job requirements and talent feedback, identify the time interval between adjacent matches, screen the records that exceed the matching interval benchmark value, and generate a matching time interval sequence;

[0096] According to the "Java programming" ability node rated as "high priority" in the node matching priority scale, collect the time points when the job requirements are released and the talent feedback is completed. For example, the requirement T1 is triggered on 04-01, and the talent "Zhang San" completes the feedback on 04-08. The requirement T2 is triggered on 04-10, and the talent completes the feedback on 04-15. Then identify the time interval between adjacent matching events, that is, the time interval between the end time point 04-08 of T1 and the trigger time point 04-10 of T2 is 2 days. Then screen the records that exceed the matching interval benchmark value. The matching interval benchmark value here is set to 3 days. This benchmark value is obtained by taking the 80th percentile of the time intervals of 1000 matching records of similar positions within the company in the past year, ensuring that the screened records are events with longer response intervals. For example, if the interval between T2 and the subsequent T3 is 4 days, since 4 > 3, this record will be screened out. Finally, combine all the screened time interval values [4, 5, 7] to generate a matching time interval sequence.

[0097] Matching cycle division sub-module: By calling the matching time interval sequence, count the continuous matching times of the same type of matching events, and calculate the cycle distribution trend value of the matching according to the matching duration, interval time and demand load index using formula (2), and establish a matching cycle distribution stage;

[0098] (2);

[0099] in, The value representing the periodic distribution trend of the matching. This represents the total number of matching events of the same type. Representing the Duration of the matching event This represents the average duration of all matched events.

[0100] The "Demand Load Index" is a key parameter used to quantify the task intensity and demand pressure of a job during the matching cycle. Its main function is to assist in assessing the density and continuity of job matching events. In the "Deep Neural Network Index Construction Module," the system generates a matching time interval sequence by collecting job demand trigger times and talent feedback times, and combines this with the "Matching Duration" and "Demand Load Index" to calculate the distribution trend of the matching cycle. Specifically, the Demand Load Index reflects a comprehensive value of dimensions such as the frequency of tasks issued by a job per unit time, task complexity, the number of key competency requirements, and their magnitude of change. For example, in high-intensity demand scenarios, a job may issue multiple highly complex tasks consecutively in a short period of time. This load will affect the talent's response rhythm and matching frequency. Therefore, by introducing this index, the system helps to more accurately assess the rhythm fluctuations and cyclical trends of matching events, thereby improving the accuracy and dynamic adaptability of matching modeling. By integrating the Demand Load Index, the system can not only characterize the dynamic intensity of job demands but also provide a basis for decision-making regarding the division and priority adjustment of job-talent matching cycles, making the matching process more targeted and real-time.

[0101] Call the matching time interval sequence [4, 5, 7], first count the number of consecutive matches of the same type of matching events (e.g., all feature development tasks that are "Java programming" skills), and record the total number of matches. Then, based on the duration of the matching, the interval, and the demand load indicators, taking the duration of the matching as an example, the duration sequence of the 10 matching events was collected. (Unit: days) is [3, 5, 4, 3, 5, 6, 2, 4, 5, 3]. The periodic distribution trend value of the matching is calculated using formula (2). The operation logic of this formula is to measure the dispersion and distribution pattern of the matching time sequence by calculating the ratio of the mean absolute deviation (numerator) to the standard deviation (square root of the denominator), which is not affected by the dimension.

[0102] First, calculate the average duration. sky;

[0103] Next, calculate the molecular part. :

[0104] ;

[0105] Then calculate the square of the denominator. :

[0106] ;

[0107] Substituting the calculation result into the formula, the provided mathematical expression is:

[0108] ;

[0109] The advantage of the formula is that it provides a standardized metric for assessing the stability of the matching activity cycle, unaffected by the specific duration. The value of 0.267 indicates that the volatility of the matching cycle is at a specific quantifiable level. Finally, the distribution phase of the matching cycle is established based on this trend value.

[0110] Cyclical Trend Classification Submodule: By calling the matching cyclical distribution stage, it identifies matching events of the same type of cyclical trend, identifies differences in trend fluctuations, classifies and archives them according to the difference threshold, and generates a matching evolutionary cyclical sequence.

[0111] The matching cycle distribution stage is invoked, which includes multiple matching cycle distribution trend values ​​calculated for different capability types (such as "feature development" and "defect repair"). For example, the trend value for "feature development" is 0.267, and the trend value for "defect repair" is 0.281. First, matching events with similar cycle trends are identified, and a difference threshold is set for classification. This difference threshold is set based on the statistical distribution of the trend value differences of 200 historical matching events, taking the 25th quantile of 0.02 to ensure the precision of classification. The trend fluctuation difference between the two groups of events is calculated as |0.267-0.281|=0.014. Since 0.014<0.02, "feature development" and "defect repair" are classified into the same trend, i.e., "stable" matching. If the difference is greater than 0.02, they are classified into another category, such as "fluctuating" matching. Finally, all matching events are archived according to their classification results to form a time series with category labels, generating a matching evolution cycle sequence.

[0112] Please see Figure 2 , 6 The matching priority adjustment module includes:

[0113] Trend sequence identification submodule: By calling the matching evolution cycle sequence, it extracts incremental change values, judges the upward trend range based on the cumulative increase and fluctuation amplitude, marks key positions and talents, and generates an automated matching trend anomaly identification list;

[0114] The matching evolution cycle sequence is called to extract the matching frequency data of the "stable" matching category in four consecutive quarters [25, 28, 35, 32]. The incremental change value is calculated as [+3, +7, -3]. The upward trend range is judged based on the cumulative increase and fluctuation range. Here, the cumulative increase threshold is set to 10 times and the fluctuation range threshold is set to 5 times. These two thresholds are set after analyzing the growth rate of matching data of similar positions in the past three years and taking their average growth rate and standard deviation. The cumulative increase from the second quarter to the third quarter is +7, which does not reach the threshold of 10, but the cumulative increase from the first quarter to the third quarter is 3+7=10, which reaches the threshold of 10. Moreover, the fluctuation range in this range is 7, which is greater than the threshold of 5. Therefore, the second to the third quarter is judged to be the upward trend range. The key positions "senior Java engineer" and talent "Zhang San" associated with this range are marked. Finally, the information is summarized to generate an automated matching trend anomaly identification list.

[0115] Response Feature Extraction Submodule: By calling the job number and talent number in the automated matching trend anomaly identification list, it identifies abnormal fluctuation values ​​and response delays within the segment. Combining the delay distribution and fluctuation frequency, it analyzes the differences in job and talent responses to obtain the automated matching response feature difference value.

[0116] The system calls upon the job ID "G-001" and talent ID "T-0086" from the automated trend anomaly identification list to identify abnormal fluctuation values ​​and response delays within the marked upward trend interval (second to third quarter). Specifically, it extracts the response delay sequence [3, 4, 3, 8, 7] days for talent "Zhang San" on the "database performance tuning" task within this interval. The abnormal fluctuation values ​​are 8 days and 7 days, with a fluctuation frequency of 0.5 times per week. Then, combining the delay distribution (mean 5 days, standard deviation 2.1 days) and the fluctuation frequency (0.5 times per week), a weighted sum is applied, resulting in a response characteristic of 0.7. The difference in response between job positions and talents is calculated by adding the mean delay to 0.3 × the fluctuation frequency, which is 0.7 × 5 + 0.3 × 0.5 = 3.65. This gives the difference in the automatic matching response characteristics.

[0117] Periodic Revision Submodule: Based on the difference value of the automatic matching response characteristics and the corresponding period of the trend segment, identify the structure of the periodic revision item, analyze the degree of periodic offset and frequency distribution amplitude, calculate the periodic revision offset using formula (3), and adjust the frequency and time distribution of job-talent matching in combination with the original periodic structure difference to obtain the job-talent matching periodic adjustment table;

[0118] ;

[0119] in, Indicates the periodic revision offset. Indicates the first The difference in response characteristics over each period, Indicates the first The expected response characteristic difference value for each cycle. For the number of cycles, This represents the average of the differences in response characteristics across all periods. Indicates the first The frequency distribution amplitude of each cycle For adjustment coefficients;

[0120] Based on the difference values ​​of the automated matching response characteristics, such as a series of difference values ​​over four consecutive periods (quarters). Given the periods [2.5, 3.1, 3.65, 3.4] and the corresponding cycles for the trend segments (second and third quarters), cycles 2 and 3 are identified as cycle revision terms. Their cycle offset and frequency distribution amplitude are analyzed. The frequency distribution amplitude here... The difference between the maximum and minimum response delay values ​​within each cycle is obtained, with the sequence being [2, 3, 5, 3]. The cycle revision offset is calculated using formula (3) and set as a baseline value of 2.8. This baseline value is the average value calculated from the historical data of 100 stable talents at the same level. The number of cycles is 4. To adjust the coefficient, it was set to 0.1.1. This coefficient was determined through 5 independent cross-validation experiments, with a step size of 0.01 in the range of 0.01 to 0.5. The final value of 0.1, which resulted in the highest accuracy of the revised matching, was selected. The logic of the formula is that the numerator calculates the absolute deviation between the measured value and the expected value, the standard deviation term in the denominator normalizes the deviation, and the product term smooths the result using the amplitude of the frequency distribution. The larger the amplitude, the smaller the degree of revision.

[0121] First, calculate the relevant parameters: ;

[0122] The square of the denominator ;

[0123] denominator Then, calculations are performed for each cycle and the results are summed:

[0124] Period 1: ;

[0125] Period 2: ;

[0126] Period 3: ;

[0127] Period 4: ;

[0128] Periodic revision offset ;

[0129] The advantage of this formula lies in its ability to not only quantify the degree of deviation in response characteristics but also innovatively introduce the frequency distribution amplitude as a dynamic adjustment factor. This makes the revision of the matching cycle more stable and closely reflects actual fluctuations. The result of 1.7578 indicates a moderate need for revision. Subsequently, based on the matching frequencies of 28 and 35 in the second and third quarters of the original cycle structure, adjustments are made according to the deviation S. For example, the frequency of the third quarter is reduced by 1.7578. 10% 35 The number of adjustments was increased from 6 to 29, resulting in the Job-Talent Matching Cycle Adjustment Table.

[0130] Please see Figure 2 , 7 The attribution analysis module includes:

[0131] The response difference extraction submodule: Based on the job-talent matching cycle adjustment table, it examines the response time and behavior change sequence of high-frequency jobs and talents, identifies the synchronization deviation between response time difference and behavior change rate, filters the time points where the deviation exceeds the stable range and the job and talent numbers, and generates an automated matching response offset list.

[0132] "Behavioral variability rate" is an indicator used to measure the magnitude of changes in an employee's behavioral performance over a specific period, reflecting the volatility of their work activity or participation. Specifically, the system analyzes time series data on behavioral data such as "daily lines of code commits," extracts trends, and calculates the magnitude of change between adjacent time points to derive the behavioral variability rate. For example, if an employee's code commits suddenly drop from 300 lines to 150 lines over several consecutive days, this drastic fluctuation is marked as a high behavioral variability rate, indicating a potential anomaly in their work performance. This indicator is used for synchronization deviation analysis with job response time differences to identify potential matching deviation nodes and abnormal fluctuation segments, providing crucial information for adjusting the accuracy of job-talent matching and tracing risks.

[0133] Based on the job-talent matching cycle adjustment table, we examined the response time and behavior change sequence of the high-frequency job "Senior Java Engineer" and talent "Zhang San" in the third quarter. Specifically, the job demand response time sequence was [2, 2, 3, 2, 8] days, and the talent behavior change sequence, quantified here as the number of lines of code committed per day, was [300, 320, 290, 310, 150] lines. We identified the synchronization deviation between the two sequences by normalizing the two sequences and calculating their correlation coefficient, which yielded a value of -0.75, indicating a significant negative correlation. We then filtered out time points where the deviation exceeded the stable interval, which was set as the correlation coefficient [-0.5, 0.5]. This interval was obtained by sampling talents without matching anomalies. Since -0.75 exceeded this interval, we selected the fifth time point (response time 8 days, number of lines of code 150) and the associated job and talent numbers to generate an automated matching response offset list.

[0134] "Daily lines of code commits," a crucial behavioral metric for measuring employee work activity and task responsiveness, is automatically collected by the system through integration with internal enterprise DevOps platforms (such as Git, GitLab, and Jenkins). The system compiles daily code change records for developers, extracts the specific lines of commits, and generates a time series. For example, an engineer's daily commits in the third quarter might be [300, 320, 290, 310, 150] lines. This data not only allows for analysis of behavioral trends but also identifies whether responses align with job requirements. This automated data collection method is more accurate and real-time than traditional methods relying on manual reporting or performance evaluation, ensuring the authenticity and dynamism of behavioral data during the job-talent matching assessment process.

[0135] The wave origin discrimination submodule: Based on the automated matching response offset list, it calls the behavior wave sequence, compares the wave amplitude and direction at the offset time point, identifies continuous and consistent wave segments and records the intervals that overlap with the offset time point, and generates a response wave origin segment table.

[0136] "Behavioral fluctuation sequence" refers to the set of continuously changing values ​​recorded by the system for a certain type of behavioral indicator (such as code submission, document viewing, learning task completion, etc.) within a specified time period. This data primarily originates from various system logs and platform records used by employees within the enterprise. For example, the number of times an employee accesses a technical documentation platform or the progress of task completion on a learning platform will be recorded as a time series for the system to use in behavioral fluctuation analysis. In identifying trend anomalies, the system compares this fluctuation data with job responses to determine if they share a common origin—that is, whether they stem from changes in the same type of behavioral pattern—thus pinpointing the root cause of abnormal employee performance responses. This application greatly enhances the system's ability to understand employee behavioral states, achieving a leap from "surface feature matching" to "deep behavioral understanding."

[0137] Based on the fifth time point recorded in the automated matching response offset list, the behavioral fluctuation sequence of talent "Zhang San" before and after that time point is called, namely the code submission line count sequence [..., 310, 150, 160, 280, ...]. The fluctuation amplitude of the offset time point (the fifth day) is |150-310|=160, and the direction is downward. Compared with the fluctuation amplitude of the next time point, which is |160-150|=10, and the direction is upward, and related behaviors, such as "technical document viewing count", whose fluctuation sequence is [..., 12, 3, 4, 10, ...], with a fluctuation amplitude of |3-12|=9, and the direction is downward, it is identified that at the fifth time point, both the "code submission line count" and "technical document viewing count" behavioral sequences show continuous and consistent downward fluctuations. This overlapping continuous and consistent fluctuation segment, namely the fourth day to the fifth day, is recorded, and a response fluctuation source segment table is generated.

[0138] Deviation attribution identification submodule: Based on the response fluctuation common source segment table, extract the node path of job position and talent number, track the response order and signal transmission of nodes in the overlapping segment, identify the response abnormal frequency of the signal source node, and form a matching deviation tracing path map;

[0139] Based on the response fluctuation source segment table, the node path between the job title "Senior Java Engineer" and the talent "Zhang San" was extracted. This path, obtained from the "Talent Capability Structure Graph," is "MySQL Database" -> "Data Access Layer Development" -> "Business Logic Implementation." Tracing the response order and signal transmission process of each node on this path within the overlapping segment from the fourth to the fifth day, it was found that on the fourth day, the "MySQL Database" node received a task signal for "Complex Query Optimization," but the response of this node (i.e., the talent completed the relevant code submission) was delayed until the end of the fifth day, and the quality score was low. Further identification revealed that the "MySQL Database" signal source node had an abnormal response frequency (defined as a delay of more than 2 days) of 4 times per month throughout the quarter, far exceeding its normal level of 1 time per month. Finally, this tracing path and the source node with the highest abnormal frequency, "MySQL Database," were highlighted to form a matching deviation tracing path graph.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A job-talent matching system based on deep integration, characterized in that: include: The graph neural network talent evaluation module is used to obtain key information on job requirements and talent characteristics, analyze the relationship between job and talent, determine the matching direction and ability chain length based on the response time difference and priority order of job requirements and talent capabilities, and perform feature weight and time delay mapping to generate a talent capability structure map. Heterogeneous data fusion module: Based on the talent capability structure map, select the capability feature values ​​and behavior frequency of core nodes, match and compare the abnormal fluctuation frequency and feature weight, identify the key matching level, and obtain the node matching priority scale; Deep Neural Network Index Construction Module: Based on the node matching priority scale, extract the time interval and duration of job requirements and talent feedback, determine the high-frequency matching distribution stage, classify the periodic trends of similar matching events, and generate a matching evolution cycle sequence. Matching Priority Adjustment Module: Calls the matching evolution cycle sequence, combines the abnormal fluctuation value and response time delay within the jurisdiction, filters the target positions and talents in the high trend segment, revises the matching cycle value in segments, and obtains the position-talent matching cycle adjustment table.

2. The job-talent matching system based on deep fusion according to claim 1, characterized in that: The graph neural network talent evaluation module includes: The competency feature extraction submodule is used to obtain key information on job requirements and talent characteristics, align multiple signals by timestamp, extract talent capability mutation points, locate changes in requirements and characteristics, identify response differences, and generate job-talent response deviation. Capability Lag Analysis Submodule: Based on the job-talent response deviation, extract the capability signal delay segment and the characteristic signal response sequence, count the delay length and response sequence, determine the signal matching direction, measure the number of capability chain trigger sequences within the lag segment, and generate the capability signal lag structure quantity. Capability chain structure generation submodule: Based on the hysteresis structure of capability signals, extract behavioral feature change points and capability chain signal linkage time periods, compare signal response frequency and time delay, identify linkage times and average delay ratio, analyze matching relationships and capability response frequency mapping, and generate talent capability structure map.

3. The job-talent matching system based on deep fusion according to claim 1, characterized in that: The heterogeneous data fusion module includes: Feature fluctuation extraction submodule: Based on the talent capability structure map, extract the capability feature values ​​of core nodes, screen sequences whose fluctuations exceed the feature fluctuation benchmark value, analyze the proportion of abnormal fluctuations, and generate the abnormal feature fluctuation frequency ratio. Frequency weight matching submodule: By calling the frequency ratio of abnormal feature fluctuations, combined with the frequency of node operations and the number of capability paths, the frequency weight comparison item is identified, the node abnormal offset is adjusted according to the proportion of tasks on the capability path, and the fluctuation coupling priority index is calculated. Priority Level Determination Submodule: Based on the fluctuation coupling priority index, extract the numerical range corresponding to the node, set the node level division interval group, assign level identifiers according to the index value falling into the interval, sort to determine the matching level, and obtain the node matching priority scale.

4. The job-talent matching system based on deep fusion according to claim 1, characterized in that: The deep neural network metric building module includes: Matching record screening submodule: Based on the node matching priority scale, collect the trigger and end time points of job requirements and talent feedback, identify the time interval between adjacent matches, filter records that exceed the matching interval benchmark value, and generate a matching time interval sequence; Matching cycle segmentation submodule: By calling the matching time interval sequence, the number of consecutive matches of the same type of matching event is counted. Based on the matching duration, interval time and demand load index, the matching cycle distribution trend value is calculated, and the matching cycle distribution stage is established. The cycle trend classification submodule identifies matching events of the same cycle trend by calling the matching cycle distribution stage, identifies differences in trend fluctuations, classifies and archives them according to the difference threshold, and generates a matching evolution cycle sequence.

5. A job-talent matching system based on deep fusion according to claim 1, characterized in that: The matching priority adjustment module includes: Trend sequence identification submodule: By calling the matching evolution cycle sequence, it extracts incremental change values, judges the upward trend range based on the cumulative increase and fluctuation amplitude, marks key positions and talents, and generates an automated matching trend anomaly identification list; Response Feature Extraction Submodule: By calling the job number and talent number in the automated matching trend anomaly identification list, it identifies abnormal fluctuation values ​​and response delays within the segment. Combining the delay distribution and fluctuation frequency, it analyzes the differences in job and talent responses to obtain the automated matching response feature difference value. The periodic revision submodule identifies the periodic revision item structure based on the difference values ​​of the automated matching response characteristics and the corresponding period of the trend segment, analyzes the degree of periodic offset and frequency distribution amplitude, calculates the periodic revision offset, and adjusts the frequency and time distribution of job-talent matching based on the differences in the original periodic structure to obtain the job-talent matching periodic adjustment table.

6. A job-talent matching system based on deep fusion according to any one of claims 1-5, characterized in that: Also includes: Attribution and source analysis module: Based on the job-talent matching cycle adjustment table, it collects the response difference and behavioral fluctuation characteristics of frequently matched jobs and talents, determines whether the fluctuation characteristics have the same origin, identifies matching deviation nodes, and forms a matching deviation source tracing path map.

7. A job-talent matching system based on deep fusion according to claim 6, characterized in that: The attribution analysis module includes: The response difference extraction submodule: Based on the job-talent matching cycle adjustment table, it examines the response time and behavior change sequence of high-frequency jobs and talents, identifies the synchronization deviation between response time difference and behavior change rate, filters the time points where the deviation exceeds the stable range and the job and talent numbers, and generates an automated matching response offset list. The wave origin discrimination submodule: Based on the automated matching response offset list, it calls the behavior wave sequence, compares the wave amplitude and direction at the offset time point, identifies continuous and consistent wave segments and records the intervals that overlap with the offset time point, and generates a response wave origin segment table. Deviation attribution identification submodule: Based on the response fluctuation common source segment table, extract the node path of job position and talent number, track the response sequence and signal transmission of nodes in the overlapping segment, identify the abnormal response frequency of the signal source node, and form a matching deviation tracing path map.

8. A job-talent matching system based on deep fusion according to claim 3, characterized in that: The formula for calculating the volatility coupling priority index is as follows: ； in, Represents a volatility coupling priority indicator. Representing the Frequency of operations per node Representing the Number of capability paths per node Representing the The frequency weight ratio of each node, The weighted value representing the frequency of node operations and the number of capability paths. Representative task percentage coefficient, This represents the total number of nodes.

9. A job-talent matching system based on deep fusion according to claim 4, characterized in that: The formula for calculating the trend value of the matched periodic distribution is as follows: ； in, The value representing the periodic distribution trend of the matching. This represents the total number of matching events of the same type. Representing the Duration of the matching event This represents the average duration of all matched events.

10. A job-talent matching system based on deep fusion according to claim 5, characterized in that: The formula for calculating the periodic revision offset is as follows: ； in, Indicates the periodic revision offset. Indicates the first The difference in response characteristics over each period, Indicates the first The expected response characteristic difference value for each cycle. For the number of cycles, This represents the average of the differences in response characteristics across all periods. Indicates the first The frequency distribution amplitude of each cycle This is for adjusting the coefficient.

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